MDS-based Multi-axial Dimensionality Reduction Model for Human Action Recognition

In this paper, we present an original and efficient method of human action recognition in a video sequence. The proposed model is based on the generation and fusion of a set of prototypes generated from different view-points of the data cube of the video sequence. More precisely, each prototype is generated by using a multidimensional scaling (MDS) based nonlinear dimensionality reduction technique both along the temporal axis but also along the spatial axis (row and column) of the binary video sequence of 2D silhouettes. This strategy aims at modeling each human action in a low dimensional space, as a trajectory of points or a specific curve, for each viewpoint of the video cube in a complementary way. A simple K-NN classifier is then used to classify the prototype, for a given viewpoint, associated with each action to be recognized and then the fusion of the classification results for each viewpoint allow us to significantly improve the recognition rate performance. The experiments of our approach have been conducted on the publicly available Weizmann data-set and show the sensitivity of the proposed recognition system to each individual viewpoint and the efficiency of our multi-viewpoint based fusion approach compared to the best existing state-of-the-art human action recognition methods recently proposed in the literature.